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The Induced Dimension Reduction method is a family of simple and fast Krylov subspace algorithms for solving large nonsymmetric linear systems. The idea behind the IDR(s) variant is to generate residuals that are in the nested subspaces of shrinking dimension s.

The function idrs() solves a general linear matrix equation

``````                     0 = C - op(X, args...)
``````

where op is a linear operator in X, for example

``````    X -> X + A*X*B (Stein equation)
``````

or

``````    X -> A*X + X*B (Sylvester equation).
``````

## Syntax

``````    X, h::ConvergenceHistory = idrs_core{T}(op, args, C::T, X0 = zero(C); s = 8, tol = sqrt(eps(anorm(C))), maxiter = length(C)^2)

The right hand side C must support vecnorm, vecdot, copy!, rand! and axpy! or import and overload
the abstract functions adot, anorm, arand! and aaxpy! from module IDRsSolver used by the procedure.
.
``````

## Synonyms

``````    idrs(A, b, ...) = idrs_core((x,A) -> A*x, (A,), b, ...) solves the linear equation equation Ax = b.
stein(A, B, C, ...) = idrs_core((X,A,B) -> X + A*X*B, (A, B), C, ...) solves the Stein equation.
syl(A, B, C, ...) = idrs_core((X,A,B) -> A*X + X*B, (A, B), C, ...) solves the Sylvester equation.
``````

## Arguments

``````   s -- dimension reduction number. Normally, a higher s gives faster convergence,
but also  makes the method more expensive.
tol -- tolerance of the method.
maxiter -- maximum number of iterations

x0 -- Initial guess.
``````

## Output

``````    X -- Approximated solution by IDR(s)
h -- Convergence history

The [`ConvergenceHistory`](https://github.com/JuliaLang/IterativeSolvers.jl/issues/6) type provides information about the iteration history.
- `isconverged::Bool`, a flag for whether or not the algorithm is converged.
- `threshold`, the convergence threshold
- `residuals::Vector`, the value of the convergence criteria at each iteration

If ||C-op(X)||_F > tol, the function gives a warning.
``````

## References

`````` IDR(s): a family of simple and fast algorithms for solving large
nonsymmetric linear systems. P. Sonneveld and M. B. van Gijzen
SIAM J. Sci. Comput. Vol. 31, No. 2, pp. 1035--1062, 2008
 Algorithm 913: An Elegant IDR(s) Variant that Efficiently Exploits
Bi-orthogonality Properties. M. B. van Gijzen and P. Sonneveld
ACM Trans. Math. Software,, Vol. 38, No. 1, pp. 5:1-5:19, 2011
 This file is a translation of the following MATLAB implementation:
http://ta.twi.tudelft.nl/nw/users/gijzen/idrs.m
 IDR(s)' webpage http://ta.twi.tudelft.nl/nw/users/gijzen/IDR.html
``````

07/16/2015

over 1 year ago

22 commits